Abstract Nonlinear dependencies among highly correlated variables of a multifaceted process system pose significant challenges for process safety assessment. The copula function is a flexible statistical tool to capture complex dependencies and interactions among process variables in the causation of process faults. An integration of the copula function with the Bayesian network provides a framework to deal with such complex dependence. This study attempts to compare the performance of the copula-based Bayesian network with that of the traditional Bayesian network in predicting failure of a multivariate time dependent process system. Normal and abnormal process data from a small-scale pilot unit were collected to test and verify performances of failure models. Results from analysis of the collected data establish that the performance of copula-based Bayesian network is robust and superior to the performance of traditional Bayesian network. The structural flexibility, consideration of non-linear dependence among variables, uncertainty and stochastic nature of the process model provide the copula-based Bayesian network distinct advantages. This approach can be further tested and implemented as an online process monitoring and risk management tool.

中文翻译：

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考虑过程变量间多元非线性相关的过程安全评估

摘要 多方面过程系统的高度相关变量之间的非线性依赖性对过程安全评估提出了重大挑战。copula 函数是一种灵活的统计工具，用于捕获导致过程故障的过程变量之间的复杂依赖关系和相互作用。copula 函数与贝叶斯网络的集成提供了一个框架来处理这种复杂的依赖关系。本研究试图比较基于 copula 的贝叶斯网络与传统贝叶斯网络在预测多元时间相关过程系统的故障方面的性能。收集了来自小规模中试装置的正常和异常过程数据，以测试和验证故障模型的性能。对收集到的数据的分析结果表明，基于 copula 的贝叶斯网络的性能是稳健的，并且优于传统贝叶斯网络的性能。过程模型的结构灵活性、对变量之间非线性相关性的考虑、不确定性和随机性为基于 copula 的贝叶斯网络提供了独特的优势。这种方法可以作为在线流程监控和风险管理工具进行进一步测试和实施。